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Methodological considerations for research on ethnopolitical violence

Published online by Cambridge University Press:  22 November 2016

Todd D. Little*
Affiliation:
Texas Tech University
*
Address correspondence and reprint requests to: Todd D. Little, Institute for Measurement, Methodology, Analysis, and Policy, Texas Tech University, 3008 18th Street, Lubbock, TX 79409-1071; E-mail: yhat@ttu.edu.

Abstract

The methodological and epistemological challenges that research on ethnopolitical violence faces are examined. This research area is fundamentally important for political reasons and for understanding, as well as subsequent interventions to ameliorate, youths’ responses to ethnopolitical violence. Advances in methods are reviewed that can overcome the obstacles placed by the various challenges. These issues are discussed in the context of the articles that comprise this Special Section.

Type
Special Section Articles
Copyright
Copyright © Cambridge University Press 2016 

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References

Adigüzel, F., & Wedel, M. (2008). Split questionnaire design for massive surveys. Journal of Marketing Research, 45, 608617.Google Scholar
Allen, J. M., & Nimon, K. (2007). Retrospective pretest: A practical technique for professional development evaluation. Journal of Industrial Teacher Education, 44, 2742.Google Scholar
Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In Marcoulides, G. A. & Schumacker, R. E. (Eds.), New developments and techniques in structural equation modeling (pp. 269296). Mahwah, NJ: Erlbaum.Google Scholar
Bray, J. H., Maxwell, S. E., & Howard, G. S. (1984). Methods of analysis with response-shift bias. Educational Measurement and Psychological Measurement, 44, 781804.CrossRefGoogle Scholar
Breetvelt, I. S., & Van Dam, F. S. (1991). Underreporting by cancer patients: The case of response-shift. Social Science & Medicine, 32, 981987.Google Scholar
Campbell, D. T., & Fiske, D. W. (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81105.Google Scholar
Carlsson, A. M. (1983). Assessment of chronic pain: I. Aspects of reliability and validity of the visual analogue scale. Pain, 16, 87101.Google Scholar
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558577.Google Scholar
Couper, M. M., Tourangeau, R., Conrad, F. G., & Singer, E. (2006). Evaluating the effectiveness of visual analog scales: A web experiment. Social Science Computer Review, 24, 227245.Google Scholar
Cronbach, L. J., & Furby, L. (1970). How we should measure “change”—Or should we? Psychological Bulletin, 74, 6880.Google Scholar
Davis, G. (2003). Using a retrospective pre-post questionnaire to determine program impact. Journal of Extension, 41, 4TOT4. Retrieved from http://joe.org/joe/2003august/tt4.php/tt5/php Google Scholar
Drennan, J., & Hyde, A. (2008). Controlling response shift bias: The use of the retrospective pre-test design in the evaluation of a master's programme. Assessment & Evaluation in Higher Education, 33, 699709.CrossRefGoogle Scholar
Enders, C. K. (2010). Applied missing data analysis. New York: Guilford Press.Google Scholar
Finkelstein, J. A., Quaranto, B. R., & Schwartz, C. E. (2014). Threats to the internal validity of spinal surgery outcome assessment: Recalibration response shift or implicit theories of change? Applied Research Quality Life, 9, 215232.Google Scholar
Flynn, D., van Schaik, P., & van Wersch, A. (2004). A comparison of multi-item Likert and visual analogue scales for the assessment of transactionally defined coping functions. European Journal of Psychological Assessment, 20, 4958.Google Scholar
Galenkamp, H., Deeg, D. J. H., Braam, A. W., & Huisman, M. (2013). How was your health 3 years ago? Predicting mortality in older adults using a retrospective change measure of self-rated health. Geriatrics & Gerontology, 13, 678686.Google Scholar
Garnier-Villarreal, M., Rhemtulla, M., & Little, T. D. (2014). Two-method planned missing designs for longitudinal research. International Journal of Behavioral Development, 38, 411422.Google Scholar
Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.Google Scholar
Graham, J. W., Taylor, B. J., & Cumsille, P. E. (2001). Planned missing data designs in the analysis of change. In Collins, L. M. & Sayer, A. G. (Eds.), New methods for the analysis of change (pp. 333353). Washington, DC: American Psychological Association.Google Scholar
Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned missing data designs in psychological research. Psychological Methods, 11, 323343.Google Scholar
Harel, O., Stratton, J., & Aseltine, R. (2015). Designed missingness to better estimate efficacy of behavioral studies—Application to suicide prevention trials. Journal of Medical Statistics and Informatics, 3, 17.Google Scholar
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Springer.Google Scholar
Hayes, M., & Patterson, D. (1921). Experimental development of the graphic rating method. Psychological Bulletin, 18, 9899.Google Scholar
Hill, L. G., & Betz, D. L. (2005). Revisiting the retrospective pretest. American Journal of Evaluation, 26, 501517.Google Scholar
Hoogstraten, J. (1985). Influence of objective measures on self-reports in a retrospective pretest-posttest design. Journal of Experimental Education, 53, 207210.Google Scholar
Howard, G. S. (1980). Response-shift bias: A problem in evaluating interventions with pre/post self-reports. Evaluation Review, 4, 93106.Google Scholar
Howard, G. S., Dailey, P. R., & Gulanick, N. A. (1979). The feasibility of informed pretest in attenuating response-shift bias. Applied Psychological Measurement, 3, 484494.Google Scholar
Howard, G. S., Millham, J., Slaten, S., & O'Donnell, L. (1981). Influence of subject response style effects on retrospective measures. Applied Psychological Measurement, 5, 89100.Google Scholar
Howard, W. J., Rhemtulla, M., & Little, T. D. (2015). Using principal component analysis (PCA) to obtain auxiliary variables for missing data estimation in large data sets. Multivariate Behavioral Research, 50, 285299.Google Scholar
Jia, F., Moore, E. W. G., Kinai, R., Crowe, K. S., Schoemann, A. M., & Little, T. D. (2014). Planned missing data design on small sample size: How small is too small? International Journal of Behavioral Development, 38, 118.Google Scholar
Johnson, D. R., Roth, V., & Young, R. (in press). Planned missing data designs in health surveys. In Proceedings of the Tenth Conference on Health Survey Research Methods. Hyattsville, MD: National Center for Health Statistics.Google Scholar
Jorgensen, T. D., Rhemtulla, M., Schoemann, A., McPherson, B., Wu, W., & Little, T. D. (2014). Optimal assignment methods in three-form planned missing data designs for longitudinal panel studies. International Journal of Behavioral Development, 38, 397410.CrossRefGoogle Scholar
Jose, P. E. (2013). Doing statistical mediation and moderation. New York: Guilford Press.Google Scholar
Joyce, C. R. B., Zutshi, D. W., Hrubes, V., & Mason, R. M. (1975). Comparison of fixed interval and Visual Analogue Scales for rating chronic pain. European Journal of Clinical Pharmacology, 8, 415420.Google Scholar
Kaplan, D. (2014). Bayesian statistics for the social sciences. New York: Guilford Press.Google Scholar
Kievit, W., Hendrikx, J., Stalmeier, P. F. M., van de Laar, M. A. F. J., Van Riel, P. L. C. M., & Adang, E. M. (2010). The relationship between change in subjective outcome and change in disease: A potential paradox. Quality Life Research, 19, 985994.Google Scholar
King-Kallimanis, B. L., Oort, F. J., Visser, M. R. M, & Sprangers, M. A. G. (2009). Structural equations modeling of health-related quality-of-life data illustrates the measurement and conceptual perspectives on response shift. Journal of Clinical Epidemiology, 62, 11571164.Google Scholar
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). San Diego, CA: Academic Press/Elsevier.Google Scholar
Lang, K. M., Chestnut, S., & Little, T. D. (2015). An R function to implement the PCA auxiliary variable approach: Institute for Measurement, Methodology, Analysis, and Policy Tech Report on Quark. Lubbock, TX: Author.Google Scholar
Lang, K. M., & Little, T. D. (2016). Principled missing data treatments. Prevention Science. Advanced online publication. doi:10.1007/s11121-016-0644-5Google Scholar
Lanza, S. T., & Cooper, B. R. (2016). Latent class analysis for developmental research. Child Development Perspectives, 10, 5964.Google Scholar
Levinson, W., Gordon, G., & Skeff, K. (1990). Retrospective versus actual pre-course self-assessments. Evaluation & the Health Professions, 13, 445452.CrossRefGoogle Scholar
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22, 555.Google Scholar
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. New York: Wiley.Google Scholar
Little, T. D. (2013). Longitudinal structural equation modeling. New York: Guilford Press.Google Scholar
Little, T. D. (2015). Methodological practice as matters of justice, justification, and the pursuit of verisimilitude. Research on Human Development. Advance online publication.CrossRefGoogle Scholar
Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (2014). On the joys of missing data. Journal of Pediatric Psychology, 39, 151162.Google Scholar
Little, T. D., Lang, K. M., Wu, W., & Rhemtulla, M. (2016). Missing data. In Cicchetti, D. (Ed.), Developmental psychopathology (3rd ed.). Hoboken, NJ: Wiley.Google Scholar
Little, T. D., & Rhemtulla, M. (2013). Planned missing data designs for developmental researchers. Child Development Perspectives, 7, 199204.Google Scholar
Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why the items versus parcels controversy needn't be one. Psychological Methods, 18, 285300.Google Scholar
Mackinnon, D. (2008). Introduction to statistical mediation analysis. New York: Routledge.Google Scholar
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross sectional analysis of longitudinal mediation. Psychological Methods, 12, 2344.Google Scholar
McPhail, S., Comans, T., & Haines, T. (2010). Evidence of disagreement between patient-perceived change and conventional longitudinal evaluation of change in health-related quality of life among older adults. Clinical Rehabilitation, 24, 10361044.Google Scholar
Mehta, P. (2015). xxM reference guide. Houston, TX: Author.Google Scholar
Mistler, S. A., & Enders, C. K. (2012). Planned missing data designs for developmental research. In Laursen, B., Little, T. D., & Card, N. (Eds.), Handbook of developmental research methods (pp. 742754). New York: Guilford Press.Google Scholar
Moore, D., & Tananis, C. A. (2009). Measuring change in a short-term educational program using a retrospective pretest design. American Journal of Evaluation, 30, 189202.Google Scholar
Muthén, L. K., & Muthén, B. O. (2012). Mplus user's guide: Statistical analysis with latent variables (7th ed.). Los Angeles: Author.Google Scholar
Nagl, M., & Farin, E. (2012). Response shift in quality of life assessment in patients with chronic back pain and chronic ischaemic heart disease. Disability & Rehabilitation, 34, 671680.Google Scholar
Nakonezney, P. A., & Rodgers, J. L. (2003). An empirical evaluation of the retrospective pretest: Are there advantages to looking back? Journal of Modern Applied Statistical Methods, 4, 240250.Google Scholar
Nakonezney, P. A., Rodgers, J. L., & Nussbaum, J. F. (2003). The effect of later life parental divorce on adult-child/older-parent solidarity: A test of the buffering hypothesis. Journal of Applied Social Psychology, 33, 11581178.Google Scholar
Pelfrey, W. V. Sr., & Pelfrey, W. V. Jr. (2009). Curriculum evaluation and revision in a nascent field: The utility of the retrospective pretest-posttest model in a homeland security program of study. Evaluation Review, 33, 5482.Google Scholar
Pratt, C. C., McGuigan, W. M., & Katzey, A. R. (2000). Measuring program outcome: Using retrospective pretest methodology. American Journal of Evaluation, 2, 341349.CrossRefGoogle Scholar
Raghunathan, T. E., & Grizzle, J. E. (1995). A split questionnaire survey design. Journal of the American Statistical Association, 90, 5463.Google Scholar
Rausch, M., & Zehetleitner, M. (2014). A comparison between a visual analogue scale and a four-point scale as measures of conscious experience of motion. Consciousness and Cognition, 28, 126140.Google Scholar
Rhemtulla, M., & Little, T. D. (2012). Planned missing data designs for research in cognitive development. Journal of Cognition and Development, 13, 425438.Google Scholar
Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 112.Google Scholar
Schwartz, C. E., Sprangers, M. A. G., Carey, A., & Reed, G. (2004). Exploring response shift in longitudinal data. Psychology and Health, 19, 5169.Google Scholar
Sharma, M., Fine, S. L., Brennan, R. T., & Betancourt, T. S. (2017). Coping and metal health outcomes among Sierra Leonean war-affected youth: Results from a longitudinal study. Development and Psychopathology, 29, 1124.Google Scholar
Sibthorp, J., Paisley, K., Gookin, J., & Ward, P. (2007). Addressing response-shift bias: Retrospective pretests in recreation research and evaluation. Journal of Leisure Research, 39, 295315.Google Scholar
Smits, N., & Vorst, H. C. M. (2007). Reducing the length of questionnaires through structurally incomplete designs: An illustration. Learning and Individual Differences, 17, 2534.Google Scholar
Swain, M. S. (2015). The effects of a planned missingness design on examinee motivation and psychometric quality (Doctoral dissertation, James Madison University).Google Scholar
Thomeé, R., Grimby, G., Wright, B. D., & Linacre, J. M. (1995). Rasch analysis of visual analog scale measurements before and after treatment of patellofemoral pain syndrome in women. Scandinavian Journal of Rehabilitation Medicine, 27, 145151.Google Scholar
van Buuren, S. (2012). Flexible imputation of missing data. Boca Raton, FL: CRC Press.Google Scholar